Biomedical subjects

Tian Zheng

Publications and source records attributed to Tian Zheng.

4 recordsLinked to original sources

PubMed2006

Self-correcting maps of molecular pathways.

Reliable and comprehensive maps of molecular pathways are indispensable for guiding complex biomedical experiments. Such maps are typically assembled from myriads of disparate research reports and are replete with inconsistencies due to variations in experimental conditions and/or errors. It is often an intractable task to manually verify internal consistency over a large collection of experimental statements. To automate large-scale reconciliation efforts, we propose a random-arcs-and-nodes model where both nodes (tissue-specific states of biological molecules) and arcs (interactions between them) are represented with random variables. We show how to obtain a non-contradictory model of a molecular network by computing the joint distribution for arc and node variables, and then apply our methodology to a realistic network, generating a set of experimentally testable hypotheses. This network, derived from an automated analysis of over 3,000 full-text research articles, includes genes that have been hypothetically linked to four neurological disorders: Alzheimer's disease, autism, bipolar disorder, and schizophrenia. We estimated that approximately 10% of the published molecular interactions are logically incompatible. Our approach can be directly applied to an array of diverse problems including those encountered in molecular biology, ecology, economics, politics, and sociology.

Bayes Theorem↗

PubMed2006

Backward genotype-trait association (BGTA)-based dissection of complex traits in case-control designs.

BACKGROUND: The studies of complex traits project new challenges to current methods that evaluate association between genotypes and a specific trait. Consideration of possible interactions among loci leads to overwhelming dimensions that cannot be handled using current statistical methods. METHODS: In this article, we evaluate a multi-marker screening algorithm--the backward genotype-trait association (BGTA) algorithm for case-control designs, which uses unphased multi-locus genotypes. BGTA carries out a global investigation on a candidate marker set and automatically screens out markers carrying diminutive amounts of information regarding the trait in question. To address the 'too many possible genotypes, too few informative chromosomes' dilemma of a genomic-scale study that consists of hundreds to thousands of markers, we further investigate a BGTA-based marker selection procedure, in which the screening algorithm is repeated on a large number of random marker subsets. Results of these screenings are then aggregated into counts that the markers are retained by the BGTA algorithm. Markers with exceptional high counts of returns are selected for further analysis. RESULTS AND CONCLUSION: Evaluated using simulations under several disease models, the proposed methods prove to be more powerful in dealing with epistatic traits. We also demonstrate the proposed methods through an application to a study on the inflammatory bowel disease.

Algorithms↗

PubMed2004

A demonstration and findings of a statistical approach through reanalysis of inflammatory bowel disease data.

We test the backward haplotype transmission association algorithm on genome-scan data previously studied by Rioux et al. [Rioux, J. D., et al. (2000) Am. J. Hum. Genet. 66, 1863-1870]. In their study, multipoint linkage methods were applied to affected sib-pairs with inflammatory bowel disease, and significant linkage evidence points to two susceptibility loci. After we apply our approach to these data with a global search accounting for both joint and marginal effects, very interesting results emerge, many of them intriguing. These results provide compelling support for the application of our approach to other data wherever applicable. Results from this project also make it clear that it is important to reinvestigate available family-based datasets that can be suitably reanalyzed. Given previously collected data in the literature, our approach, with its increased efficiency in using available resources, draws additional crucial information that may lead to novel and surprising results.

Genetic Linkage↗

PubMed2002

Backward Haplotype Transmission Association (BHTA) algorithm - a fast multiple-marker screening method.

The mapping of complex traits is one of the most important and central areas of human genetics today. Recent attention has been focused on genome scans using a large number of marker loci. Because complex traits are typically caused by multiple genes, the common approaches of mapping them by testing markers one after another fail to capture the substantial information of interactions among disease loci. Here we propose a backward haplotype transmission association (BHTA) algorithm to address this problem. The algorithm can administer a screening on any disease model when case-parent trio data are available. It identifies the important subset of an original larger marker set by eliminating the markers of least significance, one at a time, after a complete evaluation of its importance. In contrast with the existing methods, three major advantages emerge from this approach. First, it can be applied flexibly to arbitrary markers, regardless of their locations. Second, it takes into account haplotype information; it is more powerful in detecting the multifactorial traits in the presence of haplotypic association. Finally, the proposed method can potentially prove to be more efficient in future genomewide scans, in terms of greater accuracy of gene detection and substantially reduced number of tests required in scans. We illustrate the performance of the algorithm with several examples, including one real data set with 31 markers for a study on the Gilles de la Tourette syndrome. Detailed theoretical justifications are also included, which explains why the algorithm is likely to select the 'correct' markers.

Algorithms↗